Rapid Provisioning of Information for Business Analytics

ABSTRACT

An approach is provided in which a system creates schema terms based upon matching input data query requirements to industry terms. In turn, the system generates a query and an associative map, which includes data organized according to the schema terms. The system executes the query, which retrieves the data from the associative map and loads the data into one or more storage areas.

BACKGROUND

The present disclosure relates to dynamically generating associativemaps and queries according to schema terms based on industry models forreal-time business analytics information provisioning.

Provisioning information for business analytics typically involvesprovisioning the information from operational data stores (ODSs) into anenterprise data warehouses (EDWs), and finally into OLAP (OnLineAnalytical Processing) storage areas for analysis. Designing the schemafor provisioning the information into the EDWs requires an understandingof data requirements of the OLAP storage areas.

Since the movement of data from the ODS's to EDWs and into the OLAPstorage areas is currently programmed by database programmers, theprocess is typically time consuming and expensive. Consequently, mostorganizations invest a substantial amount of money and up-front time toevaluate current requirements and anticipate future requirements forbusiness analytics information provisioning.

BRIEF SUMMARY

According to one embodiment of the present disclosure, an approach isprovided in which a system creates schema terms based upon matchinginput data query requirements to industry terms. In turn, the systemgenerates a query and an associative map, which includes data organizedaccording to the schema terms. The system executes the query, whichretrieves the data from the associative map and loads the data into oneor more storage areas.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations, and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the present disclosure,as defined solely by the claims, will become apparent in thenon-limiting detailed description set forth below.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The present disclosure may be better understood, and its numerousobjects, features, and advantages made apparent to those skilled in theart by referencing the accompanying drawings, wherein:

FIG. 1 is a diagram showing a system dynamically creating an associativemap that includes provisioned information available to a user forbusiness analytics;

FIG. 2 is a diagram showing a feed adapter IDE generating schema termsby matching user data requirements with industry terms;

FIG. 3 is a diagram showing a feed adapter IDE generating a mappingscript;

FIG. 4 is a flowchart showing steps taken in creating schema terms andusing the schema terms to dynamically generate an associative map and aquery;

FIG. 5 is a flowchart showing steps taken in dynamically generating aschema terms dictionary;

FIG. 6 is a flowchart showing steps taken in generating a mapping scriptand a corresponding associative map based upon schema terms generatedfrom user data requirements;

FIG. 7 is a flowchart showing steps taken in generating a query scriptand exporting data to online analytical processing storage areas;

FIG. 8 is a block diagram of a data processing system in which themethods described herein can be implemented; and

FIG. 9 provides an extension of the information handling systemenvironment shown in FIG. 8 to illustrate that the methods describedherein may be performed on a wide variety of information handlingsystems that operate in a networked environment.

DETAILED DESCRIPTION

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various embodiments with various modifications as are suited to theparticular use contemplated.

As will be appreciated by one skilled in the art, aspects of the presentdisclosure may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present disclosure may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present disclosure are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The following detailed description will generally follow the summary ofthe disclosure, as set forth above, further explaining and expanding thedefinitions of the various aspects and embodiments of the disclosure asnecessary.

FIG. 1 is a diagram showing a system dynamically creating an associativemap that includes provisioned information available to a user forbusiness analytics. System 100 generates schema terms 148 by matchinguser data requirements (stored in requirements store 120) with industryterms 118 included in industry models 115. As such, system 100 utilizesschema terms 148 as column names and row tags to map information storedin operational data stores 150 into an associative map that is stored inassociative map store 160 (e.g., an HBase database). In addition,analytic applications developers (user 125) access schema terms 148 fromthe schema term dictionary 130 to view available data types and writequery scripts 175 accordingly.

Feed adapters integrated development environment (IDE) 110 retrievesgeneral user data requirements from requirements store 120 that, forexample, may be standard industry requirements (e.g., healthcareindustry requirements). Feed adapters IDE 110 matches each of thegeneral user data requirements with standard industry terms 118 includedin industry models 115. For each match, feed adapters IDE 110 stores aschema term (schema terms 148) corresponding to the matched industryterm in schema terms dictionary 130 (see FIG. 2 and corresponding textfor further details). When one of the general user data requirementsdoes not match one of industry terms 118, feed adapters IDE 110 createsa new schema term (included in schema terms 148) and stores the newschema term in schema terms dictionary 130. System 100 proceeds toevaluate the general user data requirements and generate/store a schematerm for each general user data requirement (see FIG. 5 andcorresponding text for further details).

In one embodiment, user 125 adds application-specific data requirementsto requirements store 120 that correspond to his/her specific datarequirements (e.g., emergency room information). In turn, feed adaptersIDE 110 retrieves the application-specific data requirements fromrequirements store 120 and checks whether schema terms dictionary 130includes a schema term for each of the application-specific datarequirements. When feed adapters IDE 110 identifies anapplication-specific data requirement that does not have a correspondingschema term stored in schema terms dictionary 130, feed adapters IDE 110proceeds through steps similar to those discussed above to match theapplication-specific data requirement with an industry term in industrymodels 115 or generate a new schema term (see FIG. 6 and correspondingtext for further details).

When each of the application-specific data requirements have acorresponding schema term stored in schema terms dictionary 130, feedadapters IDE 110 creates mapping script 145, which includes the schematerms as column names and/or row tags (see FIG. 3 and corresponding textfor further details). Feed adapters 140 (e.g., application program)loads mapping script 145 and generates an associative map retrieved fromdata stored in operational data stores 150. In turn, feed adapters 140stores the associative map in associative map store 160.

User 125 uses map/reduce applications IDE 170 to view the schema termsin schema terms dictionary 130 and select application-specific schematerms to generate queries (query scripts 175). In turn, map/reduceapplications 180 loads query scripts 175 and extracts data from theassociative map stored in associative map store 160 and loads therequested data into online analytical processing 190's storage areas.

In one embodiment, when user 125 wishes to generate a new query with newtypes of data, user 125 stores new application-specific datarequirements in requirements store 120 and, in turn, feed adapters IDE110 generates new application-specific schema terms 148 and mappingscript 145, which dynamically creates a new associative map inassociative map store 160. User 125 may then access the newapplication-specific schema terms via map/reduce applications IDE 170and create a new query script 175, which extracts the new data from thenew associative map and loads the new data in online analyticalprocessing 190's storage areas.

FIG. 2 is a diagram showing a feed adapter IDE generating schema termsfrom matching user data requirements with industry terms. Feed adaptersIDE 110 retrieves user data requirements (general and/orapplication-specific) from requirements 120 and matches them againstindustry models 115. The example shown in FIG. 2 shows that that theuser data requirements match industry terms 200, 210, 220, and 230. Assuch, feed adapters IDE 110 creates and stores schema terms 240, 250,260, and 270 in schema terms dictionary 130.

In one embodiment, the schema terms include directory hierarchyinformation such that allows a mapping script to instruct feed adapters140 to create an organized associative map. For example, schema terms250 and 260 correspond to industry terms 210 and 220, respectively. Eachof schema terms 250 and 260 include “customer->demographic information”directories that correspond to locations of industry terms 210 and 220in industry models 115. As such, an associative map may be generatedthat groups schema terms 250 and 260 into a “customer->demographicinformation” grouping (see FIG. 3 and corresponding text for furtherdetails).

FIG. 3 is a diagram showing a feed adapter IDE generating a mappingscript that instructs a feed adapter to generate an associative map fromdata stored in operational data stores 150. Feed adapters IDE 110 usesschema terms stored in schema terms dictionary 130 to create mappingscript 145. Feed adapters 140 retrieves data from operational datastores 150 and generates associative map 300. Associative map 300includes the schema terms as column names and also includes the schematerms as row tags where applicable (e.g., contact information shown inFIG. 3). As can be seen, associative map 300 includes an organizedmapping that groups “age group” and “income group” under “demographicinformation” according to entries 250 and 260 shown in FIG. 2.

FIG. 4 is a flowchart showing steps taken in creating schema terms andusing the schema terms to dynamically generate an associative map and aquery.

Processing commences at 400, whereupon a feed adapter IDE generates aschema terms dictionary (stored in schema terms dictionary 130) basedupon industry models 115 and general user data requirements stored inrequirements store 120 (pre-defined process block 410, see FIG. 5 andcorresponding text for further details). The general user datarequirements may be industry standard requirements that are utilizedacross a number of specialized fields (e.g., general health carerequirements). The feed adapter IDE matches the general user datarequirements to industry terms included in industry models 115 andcreates a schema term corresponding to the matched industry term and/orunmatched general user data requirements.

Next, the feed adapter IDE matches application-specific datarequirements included in requirements store 120 to the industry termsand creates additional application-specific schema terms accordingly. Inturn, the feed adapter IDE creates a mapping script using the schematerms and sends the script to one or more feed adapters, which executesthe mapping script program and creates an associative map (stored inassociative map store 160) by extracting data from operational datastores 150 according to the schema terms (pre-defined process block 420,see FIG. 6 and corresponding text for further details).

User 125 interfaces with a map/reduce applications IDE to view theschema terms stored in schema terms dictionary 130 and dynamicallycreates a query script. User 125 may dynamically create the query scriptbecause the schema terms identify data available in associative mapstore 160. In turn, map/reduce applications 180 retrieve data from theassociative map and load the data into online analytical processing190's storage areas (pre-defined process block 430, see FIG. 7 andcorresponding text for further details). Processing ends at 440.

FIG. 5 is a flowchart showing steps taken in dynamically generating aschema terms dictionary. Processing creates the schema terms dictionaryfrom industry terms, which are utilized as associative map key names(e.g., for key value pairs) and associative map column names during thegeneration of an associative map script (see FIG. 6 and correspondingtext for further details).

Processing commences at 500, whereupon processing selects a firstgeneral user data requirement from requirements 120 at step 510. At step520, processing matches the selected general user data requirement withindustry terms included in industry models 115 using feed adapters IDE110. In one embodiment, processing presents a list of matching industryterms to a feed adapter developer in order for the feed adapterdeveloper to select a suitable industry term to correspond with theselected general user data requirement.

A determination is made as to whether processing matched the generaluser data requirement with one of the industry terms (decision 530). Ifthere is a match, decision 530 branches to the “Yes” branch, whereuponprocessing creates a schema term that corresponds to the matchedindustry term that, in one embodiment, includes directory hierarchyinformation (step 540). Processing stores the schema term in schematerms dictionary 130.

On the other hand, if no match exists between the general user datarequirement and the industry terms (e.g., glossary model terms),decision 530 branches to the “No” branch, whereupon processing generatesa new schema term based upon the general user data requirement andstores the new schema term in schema terms dictionary 130 at step 550.In one embodiment, the new schema term may be selected by a feed adapterdeveloper.

A determination is made as to whether there are more general user datarequirements to process in requirements 120 (decision 570). If there aremore general user data requirements to process, decision 570 branches tothe “Yes” branch, which loops back to select and process the nextgeneral user data requirement. This looping continues until there are nomore general user data requirements to process, at which point decisions570 branches to the “No” branch and returns at 580.

FIG. 6 is a flowchart showing steps taken in generating and executing amapping script to create an associative map based upon schema termsgenerated from user data requirements.

Processing commences at 600, whereupon a feed adapter IDE selects afirst requirement (general user data requirement or application-specificdata requirement) from requirements store 120 at step 610. At step 615,processing accesses schema terms dictionary 130 to check whether anexisting schema term corresponds to the selected requirement. In oneembodiment, although schema terms dictionary 130 was generated basedupon general user data requirements (see FIG. 5), a user may haveincluded application-specific data requirements in requirements store120 that are particular to a business application.

A determination is made as to whether the selected requirement isrepresented in schema terms dictionary 130 (decision 620). If schematerms dictionary 130 includes a schema term that corresponds to theselected requirement, decision 620 branches to the “Yes” branch,bypassing step 625. On the other hand, if no schema term exists,decision 620 branches to the “No” branch, whereupon the feed adapter IDEgenerates a new schema term by matching the selected requirement toindustry terms (e.g., glossary models) (see FIG. 5 and correspondingtext for further details).

A determination is made as to whether there are more requirements forwhich to evaluate (decision 630). If there are more requirements forwhich to evaluate, decision 630 branches to the “Yes” branch, whichloops back to select and process the next requirement. This loopingcontinues until there are no more requirements to evaluate, at whichpoint decision 630 branches to the “No” branch.

At step 640, the feed adapter IDE generates a mapping script accordingto the schema terms stored in schema terms dictionary 130. In oneembodiment, the mapping scripts may include Java programs created by afeed adapter developer utilizing a feed adapter IDE, JDBC (Java DataBase Connectivity) API's, associative map APIs, and schema termsdictionary names. At step 645, the feed adapter IDE sends the generatedmapping script to the feed adapter to execute. Feed adapter IDEprocessing returns at 650.

Feed adapter processing commences at 660, whereupon the feed adapterreceives the mapping script at step 670. The feed adapter processes thescript and, at step 680, generates an associative map (includes schematerms as column names and row keys) by extracting data from operationaldata stores 150 and loading the data in associative map store 160according to the schema terms description arrangement (see FIG. 3 andcorresponding text for further details). For example, as discussedabove, the script may include Java programs involving JDBC API's,associative map APIs, and schema terms dictionary names. Processingreturns at 690.

FIG. 7 is a flowchart showing steps taken in generating a query scriptfrom schema terms and exporting data to online analytical processingstorage areas according to the query script.

Processing commences at 700, whereupon a map/reduce applications IDE(e.g., IDE 170 shown in FIG. 1) receives input from user 125 to generatea query (step 710). The map/reduce applications IDE provides schematerms from schema terms dictionary 130 to user 125 such that user 125may select particular schema terms to include in a query. At step 720,the map/reduce applications IDE generates a query script that includesthe schema terms and provides the query script (e.g., query script 175)to a map/reduce application.

At step 730, the map/reduce application loads the script and exportsdata from the associative map stored in associative map store 160 toonline analytical processing 190's storage areas for business analyticsevaluation by user 125. Processing returns at 740.

FIG. 8 illustrates information handling system 800, which is asimplified example of a computer system capable of performing thecomputing operations described herein. Information handling system 800includes one or more processors 810 coupled to processor interface bus812. Processor interface bus 812 connects processors 810 to Northbridge815, which is also known as the Memory Controller Hub (MCH). Northbridge815 connects to system memory 820 and provides a means for processor(s)810 to access the system memory. Graphics controller 825 also connectsto Northbridge 815. In one embodiment, PCI Express bus 818 connectsNorthbridge 815 to graphics controller 825. Graphics controller 825connects to display device 830, such as a computer monitor.

Northbridge 815 and Southbridge 835 connect to each other using bus 819.In one embodiment, the bus is a Direct Media Interface (DMI) bus thattransfers data at high speeds in each direction between Northbridge 815and Southbridge 835. In another embodiment, a Peripheral ComponentInterconnect (PCI) bus connects the Northbridge and the Southbridge.Southbridge 835, also known as the I/O Controller Hub (ICH) is a chipthat generally implements capabilities that operate at slower speedsthan the capabilities provided by the Northbridge. Southbridge 835typically provides various busses used to connect various components.These busses include, for example, PCI and PCI Express busses, an ISAbus, a System Management Bus (SMBus or SMB), and/or a Low Pin Count(LPC) bus. The LPC bus often connects low-bandwidth devices, such asboot ROM 896 and “legacy” I/O devices (using a “super I/O” chip). The“legacy” I/O devices (898) can include, for example, serial and parallelports, keyboard, mouse, and/or a floppy disk controller. The LPC busalso connects Southbridge 835 to Trusted Platform Module (TPM) 895.Other components often included in Southbridge 835 include a DirectMemory Access (DMA) controller, a Programmable Interrupt Controller(PIC), and a storage device controller, which connects Southbridge 835to nonvolatile storage device 885, such as a hard disk drive, using bus884.

ExpressCard 855 is a slot that connects hot-pluggable devices to theinformation handling system. ExpressCard 855 supports both PCI Expressand USB connectivity as it connects to Southbridge 835 using both theUniversal Serial Bus (USB) the PCI Express bus. Southbridge 835 includesUSB Controller 840 that provides USB connectivity to devices thatconnect to the USB. These devices include webcam (camera) 850, infrared(IR) receiver 848, keyboard and trackpad 844, and Bluetooth device 846,which provides for wireless personal area networks (PANs). USBController 840 also provides USB connectivity to other miscellaneous USBconnected devices 842, such as a mouse, removable nonvolatile storagedevice 845, modems, network cards, ISDN connectors, fax, printers, USBhubs, and many other types of USB connected devices. While removablenonvolatile storage device 845 is shown as a USB-connected device,removable nonvolatile storage device 845 could be connected using adifferent interface, such as a Firewire interface, etcetera.

Wireless Local Area Network (LAN) device 875 connects to Southbridge 835via the PCI or PCI Express bus 872. LAN device 875 typically implementsone of the IEEE 802.11 standards of over-the-air modulation techniquesthat all use the same protocol to wireless communicate betweeninformation handling system 800 and another computer system or device.Optical storage device 890 connects to Southbridge 835 using Serial ATA(SATA) bus 888. Serial ATA adapters and devices communicate over ahigh-speed serial link. The Serial ATA bus also connects Southbridge 835to other forms of storage devices, such as hard disk drives. Audiocircuitry 860, such as a sound card, connects to Southbridge 835 via bus858. Audio circuitry 860 also provides functionality such as audioline-in and optical digital audio in port 862, optical digital outputand headphone jack 864, internal speakers 866, and internal microphone868. Ethernet controller 870 connects to Southbridge 835 using a bus,such as the PCI or PCI Express bus. Ethernet controller 870 connectsinformation handling system 800 to a computer network, such as a LocalArea Network (LAN), the Internet, and other public and private computernetworks.

While FIG. 8 shows one information handling system, an informationhandling system may take many forms. For example, an informationhandling system may take the form of a desktop, server, portable,laptop, notebook, or other form factor computer or data processingsystem. In addition, an information handling system may take other formfactors such as a personal digital assistant (PDA), a gaming device, ATMmachine, a portable telephone device, a communication device or otherdevices that include a processor and memory.

The Trusted Platform Module (TPM 895) shown in FIG. 8 and describedherein to provide security functions is but one example of a hardwaresecurity module (HSM). Therefore, the TPM described and claimed hereinincludes any type of HSM including, but not limited to, hardwaresecurity devices that conform to the Trusted Computing Groups (TCG)standard, and entitled “Trusted Platform Module (TPM) SpecificationVersion 1.2.” The TPM is a hardware security subsystem that may beincorporated into any number of information handling systems, such asthose outlined in FIG. 9.

FIG. 9 provides an extension of the information handling systemenvironment shown in FIG. 8 to illustrate that the methods describedherein can be performed on a wide variety of information handlingsystems that operate in a networked environment. Types of informationhandling systems range from small handheld devices, such as handheldcomputer/mobile telephone 910 to large mainframe systems, such asmainframe computer 970. Examples of handheld computer 910 includepersonal digital assistants (PDAs), personal entertainment devices, suchas MP3 players, portable televisions, and compact disc players. Otherexamples of information handling systems include pen, or tablet,computer 920, laptop, or notebook, computer 930, workstation 940,personal computer system 950, and server 960. Other types of informationhandling systems that are not individually shown in FIG. 9 arerepresented by information handling system 980. As shown, the variousinformation handling systems can be networked together using computernetwork 900. Types of computer network that can be used to interconnectthe various information handling systems include Local Area Networks(LANs), Wireless Local Area Networks (WLANs), the Internet, the PublicSwitched Telephone Network (PSTN), other wireless networks, and anyother network topology that can be used to interconnect the informationhandling systems. Many of the information handling systems includenonvolatile data stores, such as hard drives and/or nonvolatile memory.Some of the information handling systems shown in FIG. 9 depictsseparate nonvolatile data stores (server 960 utilizes nonvolatile datastore 965, mainframe computer 970 utilizes nonvolatile data store 975,and information handling system 980 utilizes nonvolatile data store985). The nonvolatile data store can be a component that is external tothe various information handling systems or can be internal to one ofthe information handling systems. In addition, removable nonvolatilestorage device 845 can be shared among two or more information handlingsystems using various techniques, such as connecting the removablenonvolatile storage device 845 to a USB port or other connector of theinformation handling systems.

While particular embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that,based upon the teachings herein, that changes and modifications may bemade without departing from this disclosure and its broader aspects.Therefore, the appended claims are to encompass within their scope allsuch changes and modifications as are within the true spirit and scopeof this disclosure. Furthermore, it is to be understood that thedisclosure is solely defined by the appended claims. It will beunderstood by those with skill in the art that if a specific number ofan introduced claim element is intended, such intent will be explicitlyrecited in the claim, and in the absence of such recitation no suchlimitation is present. For non-limiting example, as an aid tounderstanding, the following appended claims contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimelements. However, the use of such phrases should not be construed toimply that the introduction of a claim element by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim element to disclosures containing only one suchelement, even when the same claim includes the introductory phrases “oneor more” or “at least one” and indefinite articles such as “a” or “an”;the same holds true for the use in the claims of definite articles.

1. A method comprising: creating, by one or more processors, one or moreschema terms based upon matching one or more data requirements to one ormore industry terms; generating, by one or more of the processors, anassociative map that includes data organized according to the one ormore schema terms; creating, by one or more of the processors, a querybased upon one or more of the schema terms; and executing the query byone or more of the processors, wherein the execution retrieves the datafrom the associative map and loads the data in one or more storageareas.
 2. The method of claim 1 further comprising: storing the one ormore schema terms in a schema terms dictionary; providing the one ormore schema terms stored in the schema terms dictionary to a user;receiving one or more schema term selections from the user that selectsone or more of the schema terms; and including the selected schema termsin the query during the creation of the query.
 3. The method of claim 2wherein the one or more storage areas are accessible to the user foranalytical processing.
 4. The method of claim 2 wherein the schema termsdictionary includes only the one or more schema terms utilized duringthe generation of the associative map.
 5. The method of claim 2 furthercomprising: generating a mapping script based upon the one or moreschema terms stored in the schema terms dictionary; and sending themapping script to one or more feed adapters.
 6. The method of claim 5further comprising: executing the mapping script by the one or more feedadapters, wherein the execution retrieves the data from one or moreoperational data storage areas and stores the data in the associativemap.
 7. The method of claim 1 further comprising: creating one or moreapplication-specific schema terms based upon matching one or moreapplication-specific data requirements to one or more of the industryterms; creating an application-specific mapping script that includes oneor more of the application-specific schema terms; executing theapplication-specific mapping script, wherein the execution of theapplication-specific mapping script retrieves application-specific datacorresponding to the one or more application-specific schema terms fromone or more operational data storage areas and adds theapplication-specific data to the associative map; providing the one ormore application-specific schema terms to a user; receiving one or moreapplication-specific schema term selections from the user that selectsone or more of the application-specific schema terms; including theselected application-specific schema terms in an application-specificquery; and executing the application-specific query, wherein theexecution of the application-specific query retrieves theapplication-specific data from the associative map and loads theapplication-specific data in one or more of the storage areas.
 8. Themethod of claim 1 wherein: the associative map is an associative maptable that includes one or more column headers corresponding to one ormore of the schema terms; and the associative map table includes one ormore row tags corresponding to one or more of the schema terms.
 9. Themethod of claim 1 wherein the one or more industry terms are retrievedfrom standardized data models corresponding to an enterprise datawarehouse.
 10. A method comprising: creating, by one or more processors,one or more schema terms based upon matching one or more general userdata requirements to one or more industry terms; retrieving one or moreapplication specific data requirements from computer memory;identifying, by one or more of the processors, one or more of theapplication specific data requirements that fail to correspond to one ofthe schema terms; creating, by one or more of the processors, one ormore application-specific schema terms based upon matching the one ormore identified application-specific data requirements to one or more ofthe industry terms; creating, by one or more of the processors, anapplication-specific mapping script that includes one or more of theapplication-specific schema terms; executing, by one or more of theprocessors, the application-specific mapping script, wherein theexecution of the application-specific mapping script retrievesapplication-specific data corresponding to the one or moreapplication-specific schema terms from one or more operational datastorage areas and includes the application-specific data in anassociative map; providing the one or more application-specific schematerms to a user; receiving one or more application-specific schema termselections from the user that selects one or more of theapplication-specific schema terms; including the selectedapplication-specific schema terms in an application-specific query; andexecuting, by one or more of the processors, the application-specificquery, wherein the execution of the application-specific query retrievesthe application-specific data from the associative map and loads theapplication-specific data in one or more storage areas accessible to theuser to perform business analytics on the loaded application-specificdata.